∗ To whom correspondence should be addressed.

We develop a theory for the temporal integration of visual motion motivated by psychophysical experiments. The theory proposes that input data are temporally grouped and used to predict and estimate the motion flows in the image sequence. This temporal grouping can be considered a generalization of the data association techniques used by engineers to study motion sequences. Our temporal-grouping theory is expressed in terms of the Bayesian generalization of standard Kalman fil-tering. To implement the theory we derive a parallel network which shares some properties of cortical networks. Computer simulations of this network demon-strate that our theory qualitatively accounts for psychophysical experiments on motion occlusion and motion outliers. In deriving our theory, we assumed spatial factorizability of the probability distributions and we made the approximation of updating the marginal distributions of velocity at each point. This allowed us to perform local computations and simplified our implementation. We argue that these approximations are suitable for the stimuli we are considering (for which spatial coherence effects are negligible). 1

Multi-Cellular Reconfigurable Circuits: Evolution Morphogenesis and Learning

Bio-inspired electronic circuits have the potential to address some of the shortcomings of conventional electronic circuits, such as lack of applicability to ill-defined problems, of robustness, or of adaptivity to unexpectedly changing environments

Acknowledgements

I owe many thanks to my adviser, Prof. Dario Floreano, who gave me both freedom and support to pursue my project. I am equally indebted to him for having adapted this thesis project in order to combine both of my passions: autonomous robotics and aviation. I would like to thank Prof. Nicolas Franceschini, Prof. Jean-Daniel Nicoud and Prof. Roland Siegwart for participating in my thesis committee. They all three were at the origin of my enthusiasm for autonomous and fly-inspired robotics. I also thank the Swiss National Science Foundation (grant nr. 620-58049) and the EPFL for funding my research over the years. I wish to express my gratitude to several former students: Tancredi Merenda, Matthijs van Leeuwen, Michael Bonani, Antoine Beyeler, Yannick Fournier, Alexis Guanella, and many others, whose work had a significant impact to the development of the software and the robotic platforms used in this thesis